CVE-2025-12487
Raw vector
CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2025-12487 is a critical-severity Reliance on Untrusted Inputs in a Security Decision (CWE-807) vulnerability in Zerodayinitiative (inferred from references). Its CVSS base score is 9.8 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 45% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as LLM Application Platforms; in the Supply Chain and Deployment risk domain.
The strongest mitigations our analysis identified map to AC-24 (Access Control Decisions) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2025-12487 affects oobabooga text-generation-webui and stems from improper handling of the trust_remote_code parameter supplied to the join endpoint. The component fails to validate the user-supplied argument before using it to load a model, allowing untrusted inputs to trigger code execution. The vulnerability is tracked as ZDI-CAN-26681 and carries a CVSS 3.0 score of 9.8.
Unauthenticated remote attackers can exploit the flaw over the network to execute arbitrary code in the context of the service account. No user interaction or credentials are required, and the attack succeeds simply by supplying a malicious value for the trust_remote_code parameter when invoking the join endpoint.
The referenced GitHub commit b5a6904c4ac4049823396090360b6f566f4e4603 addresses the issue in the codebase, while the Zero Day Initiative advisory ZDI-25-982 provides coordinated disclosure details for affected deployments.
The associated EPSS score rose from a baseline of 0.0136 to a peak of 0.0284, indicating emerging exploitation interest after public disclosure of this AI/ML-oriented web interface.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-38158
Vulnerability Data
oobabooga text-generation-webui trust_remote_code Reliance on Untrusted Inputs Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of oobabooga text-generation-webui. Authentication is not required to exploit this vulnerability. The specific flaw exists within the…
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handling of the trust_remote_code parameter provided to the join endpoint. The issue results from the lack of proper validation of a user-supplied argument before using it to load a model. An attacker can leverage this vulnerability to execute code in the context of the service account. Was ZDI-CAN-26681.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: text-generation-webui
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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- 1 hardening rule · 1 OS baseline
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Mitigating Controls (NIST 800-53 r5) AI
Explicitly requires that every access decision be based on authoritative, protected decision data rather than caller-supplied inputs.
Input validation can reject malformed or attacker-controlled values before they reach a security decision point.
Access enforcement requires decisions to be made from trusted policy data rather than modifiable client-supplied inputs.
Information-flow enforcement applies rules to validated, internal attributes instead of untrusted external values.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Strong authentication mechanisms directly prevent security decisions from depending on modifiable, untrusted inputs.
Verification and protection of identity assertions stops reliance on attacker-controlled values for authorization decisions.
Proper management of identities/credentials reduces the chance that security decisions will be driven by untrusted inputs.
Policy-driven, least-privilege authorization reduces opportunities to bypass controls via tampered inputs.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Secure coding standards directly prohibit using untrusted inputs for security-critical decisions.
Security testing in development catches input-validation flaws before deployment.
Monitoring can detect exploitation but does not stop the underlying weakness.
Application security requirements explicitly call for validation of inputs used in security decisions.
Secure architecture principles mandate treating all external inputs as untrusted.
Access-control rules can be bypassed if they rely on untrusted inputs.